{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "from yellowbrick.cluster import InterclusterDistance, KElbowVisualizer, SilhouetteVisualizer\n",
    "\n",
    "mpl.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yellowbrick &mdash; Clustering Evaluation Examples\n",
    "\n",
    "The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the `Visualizer`. Visualizers allow models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data.\n",
    "\n",
    "In machine learning, clustering models are unsupervised methods that attempt to detect patterns in unlabeled data. There are two primary classes of clustering algorithms: *agglomerative* clustering which links similar data points together, and *centroidal* clustering which attempts to find centers or partitions in the data.\n",
    "\n",
    "Currently, Yellowbrick provides several visualizers to evaluate *centroidal* mechanisms, particularly K-Means clustering, that help users discover an optimal $K$ parameter in the clustering metric:\n",
    "- `KElbowVisualizer` &mdash; visualizes the clusters according to a scoring function, looking for an \"elbow\" in the curve\n",
    "- `SilhouetteVisualizer` &mdash; visualizes the silhouette scores of each cluster in a single model\n",
    "- `InterclusterDistance` &mdash; visualizes the relative distance and size of clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data\n",
    "\n",
    "For `KElbowVisualizer` and `SilhouetteVisualzer` examples, we'll use scikit-learn's `make_blobs()` function to create a sample two-dimensional dataset with 8 random clusters of points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate synthetic dataset with 8 blobs\n",
    "X, y = make_blobs(n_samples=1000, n_features=15, centers=8, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elbow Method \n",
    "\n",
    "K-Means is a simple unsupervised machine learning algorithm that groups data into the number $K$ of clusters specified by the user, even if it is not the optimal number of clusters for the dataset. \n",
    "\n",
    "Yellowbrick's `KElbowVisualizer` implements the “elbow” method of selecting the optimal number of clusters by fitting the K-Means model with a range of values for $K$. If the line chart looks like an arm, then the “elbow” (the point of inflection on the curve) is a good indication that the underlying model fits best at that point.\n",
    "\n",
    "In the following example, the `KElbowVisualizer` fits the model for a range of $K$ values from 4 to 11, which is set by the parameter `k=(4,12)`. When the model is fit with 8 clusters we can see an \"elbow\" in the graph, which in this case we know to be the optimal number since we created our synthetic dataset with 8 clusters of points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1f805fc9e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer\n",
    "model = KMeans()\n",
    "visualizer = KElbowVisualizer(model, k=(4,12))\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.show()    # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the scoring parameter `metric` is set to `distortion`, which computes the sum of squared distances from each point to its assigned center. However, two other metrics can also be used with the `KElbowVisualizer`&mdash;`silhouette` and `calinski_harabasz`. The `silhouette` score is the mean silhouette coefficient for all samples, while the `calinski_harabasz` score computes the ratio of dispersion between and within clusters.\n",
    " \n",
    "The `KElbowVisualizer` also displays the amount of time to fit the model per $K$, which can be hidden by setting `timings=False`. In the following example, we'll use the `calinski_harabasz` score and hide the time to fit the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1f7de501d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer \n",
    "model = KMeans()\n",
    "visualizer = KElbowVisualizer(model, k=(4,12), metric='calinski_harabasz', timings=False)\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.show()    # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to remember that the Elbow method does not work well if the data is not very clustered. In this case, you might see a smooth curve and the optimal value of $K$ will be unclear.\n",
    "\n",
    "You can learn more about the Elbow method at Robert Grove's [Blocks](https://bl.ocks.org/rpgove/0060ff3b656618e9136b)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Silhouette Visualizer \n",
    "\n",
    "Silhouette analysis can be used to evaluate the density and separation between clusters. The score is calculated by averaging the silhouette coefficient for each sample, which is computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between -1 and +1, where scores near +1 indicate high separation and scores near -1 indicate that the samples may have been assigned to the wrong cluster.\n",
    "\n",
    "The `SilhouetteVisualizer` displays the silhouette coefficient for each sample on a per-cluster basis, allowing users to visualize the density and separation of the clusters. This is particularly useful for determining cluster imbalance or for selecting a value for $K$ by comparing multiple visualizers.\n",
    "\n",
    "Since we created the sample dataset for these examples, we already know that the data points are grouped into 8 clusters. So for the first `SilhouetteVisualizer` example, we'll set $K$ to 8 in order to show how the plot looks when using the optimal value of $K$. \n",
    "\n",
    "Notice that graph contains homogeneous and long silhouettes. In addition, the vertical red-dotted line on the plot indicates the average silhouette score for all observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1f7dd41b38>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer \n",
    "model = KMeans(8)\n",
    "visualizer = SilhouetteVisualizer(model)\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.show()    # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the next example, let's see what happens when using a non-optimal value for $K$, in this case, 6. \n",
    "\n",
    "Now we see that the width of clusters 1 and 2 have both increased and their silhouette coefficient scores have dropped. This occurs because the width of each silhouette is proportional to the number of samples assigned to the cluster. The model is trying to fit our data into a smaller than optimal number of clusters, making two of the clusters larger (wider) but much less cohesive (as we can see from their below-average scores)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1f7dd28e10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the clustering model and visualizer \n",
    "model = KMeans(6)\n",
    "visualizer = SilhouetteVisualizer(model)\n",
    "\n",
    "visualizer.fit(X)    # Fit the data to the visualizer\n",
    "visualizer.show()    # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intercluster Distance Maps\n",
    "\n",
    "Intercluster distance maps display an embedding of the cluster centers in 2 dimensions with the distance to other centers preserved, e.g. the closer two centers are in the visualization, the closer they are in the original feature space.\n",
    "\n",
    "The clusters are sized according to a scoring metric. By default, they are sized by membership, e.g. the number of instances that belong to each center. This gives a sense of the relative importance of clusters. Note however, that because two clusters overlap in the 2D space, it does not imply that they overlap in the original feature space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1f7dc698d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate synthetic dataset with 12 random clusters\n",
    "X, y = make_blobs(n_samples=1000, n_features=12, centers=12, random_state=42)\n",
    "\n",
    "# Instantiate the clustering model and visualizer\n",
    "model = KMeans(6)\n",
    "visualizer = InterclusterDistance(model)\n",
    "\n",
    "visualizer.fit(X)        # Fit the data to the visualizer\n",
    "visualizer.show()        # Finalize and render the figure"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
